Date: 25 October 2023
Time: 10:00 AM ET (New York Time)
Speaker(s): Dr. Mattia Merluzzi
Original Article (Free to download on the day of the webinar for 48 hours)
The research on 6G has kicked-off all around the world, opening the floor for real paradigm shifts. While the question of what will 6G be is still completely open. It is unanimously agreed that Artificial Intelligence (AI) and Machine Learning (ML) will be native components of such networks. This comes with two facets: i) Learning to communicate, by which AI/ML tools are exploited for flexible and autonomous network optimization, and ii) Communicating to learn, by which 6G is exploited as an efficient AI platform. Focusing on the latter, this webinar aims at providing vision, challenges, and tools that will effectively and efficiently enable ML/AI at the edge of wireless networks, with target performance in terms of energy, delay, learning/inference reliability. Starting from the concept that edge learning and inference are computation offloading services supported by Multi-access Edge Computing, a journey through the joint optimization of radio and computing resources will be presented, with target performance in terms of energy and delay. Performance trade-offs will be discussed through theoretical and simulation-based findings. Building on the acquired vision and tools, the webinar will then focus on edge inference, which requires a third axis: inference reliability (e.g., accuracy and confidence).
Mattia Merluzzi (SM ’17, M’21) received the M.S. degree in Telecommunication Engineering and the Ph.D. degree in Information and Communication Technologies from Sapienza University of Rome, Italy, in 2017 and 2021, respectively.
He is currently a research engineer at CEA-Leti, Grenoble, France, where he is involved in several European funded projects in the field of future 6G communication networks, with topics covering, among the others, connecting intelligence, edge computing, edge learning, and reconfigurable intelligent surfaces.
Dr. Merluzzi participated in the H2020 EU/Japan project 5G-Miedge, the H2020 EU/Taiwan project 5G CONNI and the MIUR funded PRIN Liquid Edge, the European 6G flagship project Hexa-X, the H2020 project RISE-6G. He co-authored around 30 publications, including IEEE journals, conferences, and different book chapters. He was the recipient of the 2021 GTTI (Italian National Group on Telecommunications and Information Theory) Award for the Best Ph.D. thesis.